TM4SF4 and LRRK2 Are Potential Therapeutic Targets in Lung and Breast Cancers through Outlier Analysis

DeepDRA: Drug repurposing using multi-omics data integration with autoencoders

Cancer treatment has become one of the biggest challenges in the world today. Different treatments are used against cancer; drug-based treatments have shown better results. On the other hand, designing new drugs for cancer is costly and time-consuming. Some computational methods, such as machine learning and deep learning, have been suggested to solve these challenges using drug repurposing. Despite the promise of classical machine-learning methods in repurposing cancer drugs and predicting responses, deep-learning methods performed better. This study aims to develop a deep-learning model that predicts cancer drug response based on multi-omics data, drug descriptors, and drug fingerprints and facilitates the repurposing of drugs based on those responses. To reduce multi-omics data's dimensionality, we use autoencoders. As a multi-task learning model, autoencoders are connected to MLPs. We extensively tested our model using three primary datasets: GDSC, CTRP, and CCLE to determine its efficacy. In multiple experiments, our model consistently outperforms existing state-of-the-art methods. Compared to state-of-the-art models, our model achieves an impressive AUPRC of 0.99. Furthermore, in a cross-dataset evaluation, where the model is trained on GDSC and tested on CCLE, it surpasses the performance of three previous works, achieving an AUPRC of 0.72. In conclusion, we presented a deep learning model that outperforms the current state-of-the-art regarding generalization. Using this model, we could assess drug responses and explore drug repurposing, leading to the discovery of novel cancer drugs. Our study highlights the potential for advanced deep learning to advance cancer therapeutic precision.

Patho-transcriptomic analysis of invasive mucinous adenocarcinoma of the lung (IMA): comparison with lung adenocarcinoma with signet ring cell features (SRCC)

Background

Invasive mucinous adenocarcinoma (IMA) comprises ∼5% of lung adenocarcinoma. There is no effective therapy for IMA when surgical resection is not possible. IMA is sometimes confused with adenocarcinoma with signet ring cell features (SRCC) pathologically since both adenocarcinomas feature tumor cells with abundant intracellular mucin. The molecular mechanisms by which such mucin-producing lung adenocarcinomas develop remain unknown.

Methods

Using a Visium spatial transcriptomics approach, we analyzed IMA and compared it with SRCC patho-transcriptomically. Combining spatial transcriptomics data with in vitro studies using RNA-seq and ChIP-seq, we assessed downstream targets of transcription factors HNF4A and SPDEF that are highly expressed in IMA and/or SRCC.

Results

Spatial transcriptomics analysis indicated that there are 6 distinct cell clusters in IMA and SRCC. Notably, two clusters (C1 and C3) of mucinous tumor cells exist in both adenocarcinomas albeit at a different ratio. Importantly, a portion of genes (e.g., NKX2-1 , GKN1 , HNF4A and FOXA3 ) are distinctly expressed while some mucous-related genes (e.g., SPDEF and FOXA2 ) are expressed in both adenocarcinomas. We determined that HNF4A induces MUC3A/B and TM4SF4 and that BI 6015, an HNF4A antagonist, suppressed the growth of IMA cells. Using mutant SPDEF that is associated with COVID-19, we also determined that an intact DNA-binding domain of SPDEF is required for SPDEF-mediated induction of mucin genes ( MUC5AC , MUC5B and AGR2 ). Additionally, we found that XMU-MP-1, a SPDEF inhibitor, suppressed the growth of IMA cells.

Conclusion

These results revealed that IMA and SRCC contain heterogenous tumor cell types, some of which are targetable.

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

Quantitative Analysis on Molecular Characteristics Evolution of Gastric Cancer Progression and Prognosis

The dynamic changes of key biological characteristics from gastric low-grade intraepithelial neoplasia (LGIN) to high-grade intraepithelial neoplasia (HGIN) to early gastric cancer (EGC) are still unclear, which greatly affect the accurate diagnosis and treatment of EGC and prognosis evaluation of gastric cancer (GC). In this study, bioinformatics methods/tools are applied to quantitatively analyze molecular characteristics evolution of GC progression, and a prognosis model is constructed. This study finds that some dysregulated differentially expressed mRNAs (DEmRNAs) in the LGIN stage may continue to promote the occurrence and development of EGC. Among the LGIN, HGIN, and EGC stages, there are differences and relevance in the transcription expression patterns of DEmRNAs, and the activation related to immune cells is very different. The biological functions continuously changed during the progression from LGIN to HGIN to EGC. The COX model constructed based on the three EGC-related DEmRNAs has GC prognostic risk prediction ability. The evolution of biological characteristics during the development of EGC mined by the authors provides new insight into understanding the molecular mechanism of EGC occurrence and development. The three-gene prognostic risk model provides a new method for assisting GC clinical treatment decisions.

Three Members of Transmembrane-4-Superfamily, TM4SF1, TM4SF4, and TM4SF5, as Emerging Anticancer Molecular Targets against Cancer Phenotypes and Chemoresistance

There are six members of the transmembrane 4 superfamily (TM4SF) that have similar topology and sequence homology. Physiologically, they regulate tissue differentiation, signal transduction pathways, cellular activation, proliferation, motility, adhesion, and angiogenesis. Accumulating evidence has demonstrated, among six TM4SF members, the regulatory roles of transmembrane 4 L6 domain family members, particularly TM4SF1, TM4SF4, and TM4SF5, in cancer angiogenesis, progression, and chemoresistance. Hence, targeting derailed TM4SF for cancer therapy has become an emerging research area. As compared to others, this review aimed to present a focused insight and update on the biological roles of TM4SF1, TM4SF4, and TM4SF5 in the progression, metastasis, and chemoresistance of various cancers. Additionally, the mechanistic pathways, diagnostic and prognostic values, and the potential and efficacy of current anti-TM4SF antibody treatment were also deciphered. It also recommended the exploration of other interactive molecules to be implicated in cancer progression and chemoresistance, as well as potential therapeutic agents targeting TM4SF as future perspectives. Generally, these three TM4SF members interact with different integrins and receptors to significantly induce intracellular signaling and regulate the proliferation, migration, and invasion of cancer cells. Intriguingly, gene silencing or anti-TM4SF antibody could reverse their regulatory roles deciphered in different preclinical models. They also have prognostic and diagnostic value as their high expression was detected in clinical tissues and cells of various cancers. Hence, TM4SF1, TM4SF4, and TM4SF5 are promising therapeutic targets for different cancer types preclinically and deserve further investigation.

LncRNA ST8SIA6-AS1 facilitates hepatocellular carcinoma progression by governing miR-651-5p/TM4SF4 axis

The oncogenic role of ST8SIA6-AS1 in different cancers was reported, including hepatocellular carcinoma (HCC). However, the underlying mechanism has not been completely explored. Real time quantitative PCR analysis was conducted to assess the ST8SIA6-AS1, miR-651-5p and TM4SF4 expression in HCC tissues and cells. Cell counting kit-8 and wound-healing migration assays were adopted to evaluate the HCC cell proliferation and migration, respectively. The expression of apoptosis-related proteins (Bax and Bcl-2) in human colorectal cancer cells (HCC) was determined by western blotting. In addition to bioinformatics analysis, RNA immunoprecipitation studies and luciferase reporter assays were undertaken to investigate the direct target relationship among ST8SIA6-AS1 and miR-651-5p or TM4SF4. Highly expressed ST8SIA6-AS1 and TM4SF4 as well as poorly expressed miR-651-5p were detected in HCC tissues and cells. Clinically, miR-651-5p expression in HCC tissues is negatively correlated with ST8SIA6-AS1 or TM4SF4. Cell functional assays demonstrated that ST8SIA6-AS1 silencing resulted in weakened proliferative and migratory capacities in HCC cells in addition to increase Bax expression and reduced Bcl-2 expression. ST8SIA6-AS1 exhibited its oncogenic function by sponging tumor suppressor miR-651-5p, and the anti-oncogenic of miR-651-5p was offset by its TM4SF4. The manipulation of ST8SIA6-AS1/miR-651-5p/TM4SF4 axis-mediated oncogenicity in HCC might shed new light on HCC diagnosis and therapy.

Identification of autophagy-related biomarkers in patients with pulmonary arterial hypertension based on bioinformatics analysis

Autophagy participates in the regulation of pulmonary arterial hypertension (PAH). However, the role of autophagy-related genes (ARGs) in the pathogenesis of the PAH is still unclear. This study aimed to identify the ARGs in PAH via bioinformatics analysis. A microarray dataset (GSE113439) was downloaded from the Gene Expression Omnibus database to identify differentially expressed ARGs (DEARGs). Protein–protein interactions network, gene ontology, and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to screen hub genes and the underlying molecular mechanisms of PAH. Finally, the mRNA expression of the hub genes was validated using the GSE53408 dataset. Twenty-six DEARGs were identified, all of which were upregulated. Enrichment analyses revealed that these DEARGs were mainly enriched in the nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway, PI3K-Akt signaling pathway, response to hypoxia, response to nutrient levels, and autophagy. Among these hub genes, the mRNA expression levels of HSP90AA1, HIF1A, MET, IGF1, LRRK2, CLTC, DNM1L, MDM2, RICTOR, and ROCK2 were significantly upregulated in PAH patients than in healthy individuals. Ten hub DEARGs were identified and may participate in the pathogenesis of the PAH via the regulation of autophagy. The present study may provide novel therapeutic targets for PAH prevention and treatment and expand our understanding of PAH.

Parkinson's disease and cancer: a systematic review and meta-analysis on the influence of lifestyle habits, genetic variants, and gender

PURPOSE

The relationship between Parkinson's disease (PD) and cancer has been debated. Gender and genetic influences on cancer development in PD is unclear.

METHODS

Using QUOROM guidelines, we conducted a systematic review and meta-analysis on potential clinical and genetic factors influencing the PD and subsequent cancer relationship. English articles published in PubMed, Web of Science, and SCOPUS from 2010 to 30 August 2020 were considered for suitability.

RESULTS

Of 46 studies identified, fourteen satisfied the inclusion criteria and were further analysed. Unadjusted risk ratios (RR) and 95% confidence intervals were computed to determine the PD and cancer relationship. PD patients have decreased subsequent cancer risks (RR = 0.87, 95% CI = 0.81-0.93), reduced risks of colon, rectal, and colorectal cancer (RR = 0.77, 95% CI = 0.63-0.94), lung cancer (RR = 0.62, 95% CI = 0.48-0.80), and increased brain cancer (R = 1.48, 95% CI = 1.02-2.13) and melanoma risk (R = 1.76, 95% CI = 1.23-2.50). Compared to idiopathic PD, LRRK2-G2019S carriers had increased general cancer risks (RR = 1.26, 95% CI = 1.09-1.46), particularly brain (RR = 2.41, 95% CI = 1.06-5.50), breast (RR = 2.57, 95% CI = 1.19-5.58), colon (RR = 1.83, 95% CI = 1.13-2.99), and haematological cancers (RR = 2.05, 95% CI = 1.07-3.92). Female PD patients have decreased general cancer risks compared to male PD patients in this analysis (RR = 0.83, 95% CI = 0.69-0.98).

CONCLUSION

PD patients have reduced risks of colon, rectal, colorectal cancer and lung cancers and increased risks of brain cancer and melanoma. LRRK2-G2019S carriers have increased cancer risks, particularly brain, breast, colon and blood cancers. Female gender was associated with reduced risks. The role of ethnicity, comorbidities, and lifestyle habits on PD patients' subsequent cancer risk should be further investigated.

Shifting Gears in Precision Oncology-Challenges and Opportunities of Integrative Data Analysis

For decades, research relating to modification of host immunity towards antitumor response activation has been ongoing, with the breakthrough discovery of immune-checkpoint blockers. Several biomarkers with potential predictive value have been reported in recent studies for these novel therapies. However, with the plethora of therapeutic options existing for a given cancer entity, modern oncology is now being confronted with multifactorial interpretation to devise “the best therapy” for the individual patient. Into the bargain come the multiverse guidelines for established and emerging diagnostic biomarkers, as well as the complex interplay between cancer cells and tumor microenvironment, provoking immense challenges in the therapy decision-making process. Through this review, we present various molecular diagnostic modalities and techniques, such as genomics, immunohistochemistry and quantitative image analysis, which have the potential of becoming powerful tools in the development of an optimal treatment regime when analogized with patient characteristics. We will summarize the underlying complexities of these methods and shed light upon the necessary considerations and requirements for data integration. It is our hope to provide compelling evidence to emphasize on the need for inclusion of integrative data analysis in modern cancer therapy, and thereupon paving a path towards precision medicine and better patient outcomes.

Discovery of a potent, highly selective, and orally bioavailable inhibitor of CDK8 through a structure-based optimisation

CDK8 is deregulated in multiple types of human cancer and is viewed as a therapeutic target for the treatment of the disease. Accordingly, the search for small-molecule inhibitors of CDK8 is being intensified. Capitalising on our initial discovery of AU1-100, a potent CDK8 inhibitor yet with a limited degree of kinase selectivity, a structure-based optimisation was carried out, with a series of new multi-substituted pyridines rationally designed, chemically prepared and biologically evaluated. Such endeavour has culminated in the identification of 42, a more potent CDK8 inhibitor with superior kinomic selectivity and oral bioavailability. The mechanism underlying the anti-proliferative effect of 42 on MV4-11 cells was studied, revealing that the compound arrested the G1 cell cycle and triggered apoptosis. The low risk of hepato- and cardio-toxicity of 42 was estimated. These findings merit further investigation of 42 as a targeted cancer therapeutic.